Timothy Tickle and Brian Haas
October 12, 2016
Part 1: Overview of laboratory prep and sequence analysis.
Part 2: Characteristics of expression data and QC.
Part 1: Plotting Single Cell RNA-Seq data.
Part 2: Evaluating and defining cell populations.
Macosko et al. 2015
Macosko et al. 2015
Source: http://mccarrolllab.com/dropseq
Zheng et al.
Zheng et al.
Based on ERCC spike-ins.
Accuracy: How well the abundance levels correlated with known spiked-in amounts.
Sensitivity: Minimum number of input RNA molecules required to detect a spike-in.
Svensson et al. 2016
Zero inflation.
Transcription stochasticity.
Higher Resolution.
Karchenko et al.
Read Counts
Counts by UMIs
# Load libraries
library(dplyr) # Dataframe manipulation
library(Matrix) # Sparse matrices
library(useful) # Corner function
library(vioplot) # Violin pots
library(scater) # Single Cell QC
library(scde) # Differential Expression
library(org.Hs.eg.db) # Gene name manipulation
library(Seurat) # Single cell General Analysis
# Load 10X data
pbmc.10X <- Read10X("./data/filtered_gene_bc_matrices/hg19")
# Memory use as a sparse matrix
object.size(pbmc.10X)
38715120 bytes
# Memory use as a dense matrix
# 18 X more
object.size(as.matrix(pbmc.10X))
709264728 bytes
# Expected raw counts (non-normalized data)
# Can give log transformed data but do not transform in setup method
pbmc.seurat <- new("seurat", raw.data=pbmc.10X)
# Display the internal pieces of the Seurat Object
slotNames(pbmc.seurat)
[1] "raw.data" "data" "scale.data"
[4] "var.genes" "is.expr" "ident"
[7] "pca.x" "pca.rot" "emp.pval"
[10] "kmeans.obj" "pca.obj" "gene.scores"
[13] "drop.coefs" "wt.matrix" "drop.wt.matrix"
[16] "trusted.genes" "drop.expr" "data.info"
[19] "project.name" "kmeans.gene" "kmeans.cell"
[22] "jackStraw.empP" "jackStraw.fakePC" "jackStraw.empP.full"
[25] "pca.x.full" "kmeans.col" "mean.var"
[28] "imputed" "mix.probs" "mix.param"
[31] "final.prob" "insitu.matrix" "tsne.rot"
[34] "ica.rot" "ica.x" "ica.obj"
[37] "cell.names" "cluster.tree" "snn.sparse"
[40] "snn.dense" "snn.k"
[1] "raw.data" "data" "scale.data"
[4] "var.genes" "is.expr" "ident"
[7] "pca.x" "pca.rot" "emp.pval"
[10] "kmeans.obj" "pca.obj" "gene.scores"
[13] "drop.coefs" "wt.matrix" "drop.wt.matrix"
[16] "trusted.genes" "drop.expr" "data.info"
[19] "project.name" "kmeans.gene" "kmeans.cell"
[22] "jackStraw.empP" "jackStraw.fakePC" "jackStraw.empP.full"
[25] "pca.x.full" "kmeans.col" "mean.var"
[28] "imputed" "mix.probs" "mix.param"
[31] "final.prob" "insitu.matrix" "tsne.rot"
[34] "ica.rot" "ica.x" "ica.obj"
[37] "cell.names" "cluster.tree" "snn.sparse"
[40] "snn.dense" "snn.k"
Raw sparse matrix
head(pbmc.seurat@raw.data)
6 x 2700 sparse Matrix of class "dgTMatrix"
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . 1 . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . 1 . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . 1 . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . 1 . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . 1 . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . 1 . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . 1 . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 1 . . . . . . . . . . . . . 1 . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FAM138A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OR4F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-34P13.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MIR1302-10 . . .
FAM138A . . .
OR4F5 . . .
RP11-34P13.7 . . .
RP11-34P13.8 . . .
AL627309.1 . . .
?seurat
Hiding within those mounds of data is knowledge that could change the life of a patient, or change the world. -– Atul Butte
# Gene names (row names)
head(row.names(pbmc.seurat@raw.data))
[1] "MIR1302-10" "FAM138A" "OR4F5" "RP11-34P13.7"
[5] "RP11-34P13.8" "AL627309.1"
length(row.names(pbmc.seurat@raw.data))
[1] 32738
# Column names
# Sample / Cell names / Barcodes
head(colnames(pbmc.seurat@raw.data))
[1] "AAACATACAACCAC" "AAACATTGAGCTAC" "AAACATTGATCAGC" "AAACCGTGCTTCCG"
[5] "AAACCGTGTATGCG" "AAACGCACTGGTAC"
length(colnames(pbmc.seurat@raw.data))
[1] 2700
# Only the corner
# The full data will be too large to see
corner(as.matrix(pbmc.seurat@raw.data))
AAACATACAACCAC AAACATTGAGCTAC AAACATTGATCAGC AAACCGTGCTTCCG
MIR1302-10 0 0 0 0
FAM138A 0 0 0 0
OR4F5 0 0 0 0
RP11-34P13.7 0 0 0 0
RP11-34P13.8 0 0 0 0
AAACCGTGTATGCG
MIR1302-10 0
FAM138A 0
OR4F5 0
RP11-34P13.7 0
RP11-34P13.8 0
# Plot genes per cell How many genes expressed per cells
complexity.per.cell <- apply(pbmc.seurat@raw.data, 2, function(x) sum(x > 0))
# Mean count per cell.
mean.count.per.cell <- apply(pbmc.seurat@raw.data, 2, function(x) mean(x))
# Gene prevalence
gene.prevalence <- apply(pbmc.seurat@raw.data, 1, function(x) sum(x > 0))
# Plot genes per cell How many genes expressed per cell
vioplot(complexity.per.cell)
stripchart(complexity.per.cell, add = TRUE, vertical = TRUE, method = "jitter",
jitter = 0.3, pch = 19)
abline(h = 200, col = "red")
abline(h = 2500, col = "blue")
title("Study Complexity")
axis(side = 1, at = 1, labels = c("Study"))
Boxplot:
Robust representation of a distribution using quantiles
Violin Plot:
Box plot with kernel density plot mirrored on sides.
plot(complexity.per.cell, mean.count.per.cell)
abline(v = 200, col = "red")
abline(h = log2(4))
hist(log2(gene.prevalence))
pbmc.seurat <- Setup(pbmc.seurat, min.cells = 3, min.genes = 200, do.logNormalize = TRUE,
total.expr = 10000, project = "Tutorial")
[1] "Performing log-normalization"
|
| | 0%
|
|====================== | 33%
|
|=========================================== | 67%
|
|=================================================================| 100%
[1] "Scaling data matrix"
|
| | 0%
|
|===== | 7%
|
|========= | 14%
|
|============== | 21%
|
|=================== | 29%
|
|======================= | 36%
|
|============================ | 43%
|
|================================ | 50%
|
|===================================== | 57%
|
|========================================== | 64%
|
|============================================== | 71%
|
|=================================================== | 79%
|
|======================================================== | 86%
|
|============================================================ | 93%
|
|=================================================================| 100%
Population based RNA-Seq
Today we are using
Say you were standing with one foot in the oven and one foot in an ice bucket. According to the percentage people, you should be perfectly comfortable. –Bobby Bragan
Filtering with gene prevalence:
How many times a gene's count is greater than or equal to an expression threshold throughout cells.
Other information that describes your measurements.
# Get gene names
mito.gene.names <- grep("^MT-", rownames(pbmc.seurat@data), value=TRUE)
# Get TSS normalized mitochodrial counts
col.total.counts <- Matrix::colSums(expm1(pbmc.seurat@data))
mito.percent.counts <- Matrix::colSums(expm1(pbmc.seurat@data[mito.gene.names, ]))/col.total.counts
# Add to seurat object as a metadata
pbmc.seurat <- AddMetaData(pbmc.seurat, mito.percent.counts, "percent.mitochodrial")
GenePlot(pbmc.seurat, "nUMI", "percent.mitochodrial")
dim(pbmc.seurat@data)
[1] 13714 2700
pbmc.seurat <- SubsetData(pbmc.seurat, subset.name = "nGene", accept.high = 2500)
pbmc.seurat <- SubsetData(pbmc.seurat, subset.name = "percent.mitochodrial", accept.high = 0.05)
dim(pbmc.seurat@data)
[1] 13714 2638
Saving the Seurat object
# How to save the intact object.
save(pbmc.seurat, file = "seurat_tutorial.Robj")
# How to retrieve the intact object.
load("seurat_tutorial.Robj")
You may need to export data to import into other applications.
# Log-scale expression matrix
write.table(as.matrix(pbmc.seurat@data), file = "seurat_data.txt")
# Study metadata
write.table(pbmc.seurat@data.info, file = "seurat_metadata.txt")
# What is the metadata so far
head(pbmc.seurat@data.info)
# Load Data
data("sc_example_counts")
data("sc_example_cell_info")
pd <- new("AnnotatedDataFrame", data=sc_example_cell_info)
rownames(pd) <- pd$Cell
example_sceset <- newSCESet(countData=sc_example_counts, phenoData=pd)
keep_feature <- rowSums(exprs(example_sceset)) > 0
example_sceset <- example_sceset[keep_feature,]
example_sceset <- calculateQCMetrics(example_sceset, feature_controls = 1:40)
Small data set of 40 cells.
corner(sc_example_counts)
Cell_001 Cell_002 Cell_003 Cell_004 Cell_005
Gene_0001 0 123 2 0 0
Gene_0002 575 65 3 1561 2311
Gene_0003 0 0 0 0 1213
Gene_0004 0 1 0 0 0
Gene_0005 0 0 11 0 0
corner(sc_example_cell_info)
Cell Mutation_Status Cell_Cycle Treatment
Cell_001 Cell_001 positive S treat1
Cell_002 Cell_002 positive G0 treat1
Cell_003 Cell_003 negative G1 treat1
Cell_004 Cell_004 negative S treat1
Cell_005 Cell_005 negative G1 treat2
plot(example_sceset, block1 = "Mutation_Status", block2 = "Treatment",
colour_by = "Cell_Cycle", nfeatures = 300, exprs_values = "counts")
plotExpression(example_sceset, rownames(example_sceset)[1:6],
x = "Mutation_Status", exprs_values = "exprs", colour = "Treatment")
scater_gui(example_sceset)
We have now
Next
VlnPlot(pbmc.seurat, c("GAPDH"))
# Plot a gene vs a gene
GenePlot(pbmc.seurat, "CD79A", "CD79B", cex.use = 1)
GenePlot(pbmc.seurat, "CD79A", "CD79B", cex.use = 1)
pbmc.seurat <- RegressOut(pbmc.seurat, latent.vars = c("nUMI", "percent.mitochodrial"))
[1] "Regressing out nUMI"
[2] "Regressing out percent.mitochodrial"
|
| | 0%
|
| | 1%
|
|= | 1%
|
|= | 2%
|
|== | 3%
|
|== | 4%
|
|=== | 4%
|
|=== | 5%
|
|==== | 6%
|
|==== | 7%
|
|===== | 7%
|
|===== | 8%
|
|====== | 9%
|
|======= | 10%
|
|======= | 11%
|
|======== | 12%
|
|======== | 13%
|
|========= | 14%
|
|========== | 15%
|
|========== | 16%
|
|=========== | 17%
|
|============ | 18%
|
|============ | 19%
|
|============= | 20%
|
|============== | 21%
|
|============== | 22%
|
|=============== | 22%
|
|=============== | 23%
|
|================ | 24%
|
|================ | 25%
|
|================= | 26%
|
|================= | 27%
|
|================== | 28%
|
|=================== | 29%
|
|=================== | 30%
|
|==================== | 30%
|
|==================== | 31%
|
|===================== | 32%
|
|===================== | 33%
|
|====================== | 33%
|
|====================== | 34%
|
|======================= | 35%
|
|======================= | 36%
|
|======================== | 36%
|
|======================== | 37%
|
|======================== | 38%
|
|========================= | 38%
|
|========================= | 39%
|
|========================== | 40%
|
|========================== | 41%
|
|=========================== | 41%
|
|=========================== | 42%
|
|============================ | 43%
|
|============================= | 44%
|
|============================= | 45%
|
|============================== | 46%
|
|=============================== | 47%
|
|=============================== | 48%
|
|================================ | 49%
|
|================================ | 50%
|
|================================= | 51%
|
|================================== | 52%
|
|================================== | 53%
|
|=================================== | 54%
|
|==================================== | 55%
|
|==================================== | 56%
|
|===================================== | 57%
|
|====================================== | 58%
|
|====================================== | 59%
|
|======================================= | 59%
|
|======================================= | 60%
|
|======================================== | 61%
|
|======================================== | 62%
|
|========================================= | 62%
|
|========================================= | 63%
|
|========================================= | 64%
|
|========================================== | 64%
|
|========================================== | 65%
|
|=========================================== | 66%
|
|=========================================== | 67%
|
|============================================ | 67%
|
|============================================ | 68%
|
|============================================= | 69%
|
|============================================= | 70%
|
|============================================== | 70%
|
|============================================== | 71%
|
|=============================================== | 72%
|
|================================================ | 73%
|
|================================================ | 74%
|
|================================================= | 75%
|
|================================================= | 76%
|
|================================================== | 77%
|
|================================================== | 78%
|
|=================================================== | 78%
|
|=================================================== | 79%
|
|==================================================== | 80%
|
|===================================================== | 81%
|
|===================================================== | 82%
|
|====================================================== | 83%
|
|======================================================= | 84%
|
|======================================================= | 85%
|
|======================================================== | 86%
|
|========================================================= | 87%
|
|========================================================= | 88%
|
|========================================================== | 89%
|
|========================================================== | 90%
|
|=========================================================== | 91%
|
|============================================================ | 92%
|
|============================================================ | 93%
|
|============================================================= | 93%
|
|============================================================= | 94%
|
|============================================================== | 95%
|
|============================================================== | 96%
|
|=============================================================== | 96%
|
|=============================================================== | 97%
|
|================================================================ | 98%
|
|================================================================ | 99%
|
|=================================================================| 99%
|
|=================================================================| 100%
[1] "Scaling data matrix"
|
| | 0%
|
|===== | 7%
|
|========= | 14%
|
|============== | 21%
|
|=================== | 29%
|
|======================= | 36%
|
|============================ | 43%
|
|================================ | 50%
|
|===================================== | 57%
|
|========================================== | 64%
|
|============================================== | 71%
|
|=================================================== | 79%
|
|======================================================== | 86%
|
|============================================================ | 93%
|
|=================================================================| 100%
set.seed(6473)
library(scone)
library(RColorBrewer)
library(scRNAseq)
data(fluidigm)
# Set assay to RSEM estimated counts
assay(fluidigm) = assays(fluidigm)$rsem_counts
metadata(fluidigm)$which_qc
[1] "NREADS" "NALIGNED"
[3] "RALIGN" "TOTAL_DUP"
[5] "PRIMER" "INSERT_SZ"
[7] "INSERT_SZ_STD" "COMPLEXITY"
[9] "NDUPR" "PCT_RIBOSOMAL_BASES"
[11] "PCT_CODING_BASES" "PCT_UTR_BASES"
[13] "PCT_INTRONIC_BASES" "PCT_INTERGENIC_BASES"
[15] "PCT_MRNA_BASES" "MEDIAN_CV_COVERAGE"
[17] "MEDIAN_5PRIME_BIAS" "MEDIAN_3PRIME_BIAS"
[19] "MEDIAN_5PRIME_TO_3PRIME_BIAS"
head(colData(fluidigm))
head(colData(fluidigm)$Coverage_Type)
# Preliminary Sample Filtering: High-Coverage Only
is_select = colData(fluidigm)$Coverage_Type == "High"
fluidigm = fluidigm[,is_select]
# Retain only detected transcripts
fluidigm = fluidigm[which(apply(assay(fluidigm) > 0,1,any)),]
# Initial Gene Filtering: Select "common" transcripts based on proportional criteria.
num_reads = quantile(assay(fluidigm)[assay(fluidigm) > 0])[4]
num_cells = 0.25*ncol(fluidigm)
is_common = rowSums(assay(fluidigm) >= num_reads ) >= num_cells
data(housekeeping)
hk = intersect(housekeeping$V1,rownames(assay(fluidigm)))
head(housekeeping$V1)
[1] "AATF" "ABL1" "ACAT2" "ACTB" "ACTG1" "ACTN4"
# Metric-based Filtering
mfilt = metric_sample_filter(assay(fluidigm),
nreads=colData(fluidigm)$NREADS,
ralign=colData(fluidigm)$RALIGN,
gene_filter=is_common,
pos_controls=rownames(fluidigm) %in% hk,
zcut=3, mixture=FALSE,
plot=FALSE)
# Simplify to a single logical
mfilt = !apply(simplify2array(mfilt[!is.na(mfilt)]),1,any)
# Cell filter
goodDat = fluidigm[,mfilt]
# Final Gene Filtering: Highly expressed in at least 5 cells
num_reads = quantile(assay(fluidigm)[assay(fluidigm) > 0])[4]
num_cells = 5
is_quality = rowSums(assay(fluidigm) >= num_reads ) >= num_cells
# Expression Data (Required)
expr = assay(goodDat)[is_quality,]
# Biological Origin - Variation to be preserved (Optional)
bio = factor(colData(goodDat)$Biological_Condition)
# Processed Alignment Metrics - Variation to be removed (Optional)
qc = colData(goodDat)[,metadata(goodDat)$which_qc]
ppq = scale(qc[,apply(qc,2,sd) > 0],center = TRUE,scale = TRUE)
# Positive Control Genes - Prior knowledge of DE (Optional)
poscon = intersect(rownames(expr),strsplit("ALS2, CDK5R1, CYFIP1, DPYSL5, FEZ1, FEZ2, MAPT, MDGA1, NRCAM, NRP1, NRXN1, OPHN1, OTX2, PARD6B, PPT1, ROBO1, ROBO2, RTN1, RTN4, SEMA4F, SIAH1, SLIT2, SMARCA1, THY1, TRAPPC4, UBB, YWHAG, YWHAH",split = ", ")[[1]])
# Negative Control Genes - Uniformly expressed transcripts (Optional)
negcon = intersect(rownames(expr),hk)
SUM_FN = function (ei)
{ # TSS
sums = colSums(ei)
eo = t(t(ei)*mean(sums)/sums)
return(eo)
}
EFF_FN = function (ei)
{ # Divided by complexity
sums = colSums(ei > 0)
eo = t(t(ei)*sums/mean(sums))
return(eo)
}
scaling=list(none=identity, #do nothing
sum = SUM_FN, # User functions
eff = EFF_FN,
tmm = TMM_FN, # SCONE
uq = UQ_FN,
uqp = UQ_FN_POS,
fq = FQT_FN,
fqp = FQ_FN_POS,
deseq=DESEQ_FN,
deseqp=DESEQ_FN_POS)
# Simple FNR model estimation with SCONE::estimate_ziber
# fnr_out = estimate_ziber(x = expr, bulk_model = TRUE,
# pos_controls = rownames(expr) %in% hk,
# maxiter = 10000)
load("data/fnr_out.Robj")
## ----- Imputation List Argument -----
imputation=list(none=impute_null, # No imputation
expect=impute_expectation)
# Replace zeroes with expected expression level
impute_args = list(p_nodrop = fnr_out$p_nodrop, mu = exp(fnr_out$Alpha[1,]))
params <- scone(expr,
imputation = imputation, impute_args = impute_args,
scaling=scaling,
qc=ppq, bio = bio,
# Negative controls for RUVg
# normalization and evaluation
ruv_negcon = negcon,
# Parameter Arguments
k_qc=3, k_ruv = 3,
adjust_bio="no",
run=FALSE)
# Visualize output params object
head(params)
apply(params,2,unique)
is_screened = ((params$imputation_method == "expect") & (params$scaling_method %in% c("none","deseqp","uqp","fqp","eff")))
params = params[!is_screened,]
head(params)
BiocParallel::register(BiocParallel::SerialParam())
res <- scone(expr,
imputation = imputation, impute_args = impute_args,
scaling=scaling,
qc=ppq, bio = bio,
ruv_negcon = negcon,
k_qc=3, k_ruv = 3,
adjust_bio="no",
eval_poscon = poscon, # Positive controls for evaluation
run=TRUE, params = params, # Additional params
eval_kclust = 2:6,stratified_pam = TRUE,
return_norm = "in_memory",
rezero = TRUE)
class:small-code
names(res)
[1] "normalized_data" "metrics" "scores" "params"
sconeReport(scone_res = res,
qc = ppq,
bio = bio,
negcon = negcon, poscon = poscon)
If you are interested in reading more about Scone feel free to visit this tutorial.
Things to be aware of.
Often t-SNE is performed on PCA components
pbmc.seurat <- MeanVarPlot(pbmc.seurat,fxn.x=expMean,fxn.y=logVarDivMean,
x.low.cutoff=0.0125,x.high.cutoff=3,
y.cutoff=0.5,do.contour=FALSE,do.plot=FALSE)
[1] "Calculating gene dispersion"
|
| | 0%
|
|===== | 7%
|
|========= | 14%
|
|============== | 21%
|
|=================== | 29%
|
|======================= | 36%
|
|============================ | 43%
|
|================================ | 50%
|
|===================================== | 57%
|
|========================================== | 64%
|
|============================================== | 71%
|
|=================================================== | 79%
|
|======================================================== | 86%
|
|============================================================ | 93%
|
|=================================================================| 100%
length(pbmc.seurat@var.genes)
[1] 1839
pbmc.seurat <- PCA(pbmc.seurat,pc.genes=pbmc.seurat@var.genes,do.print=FALSE)
# Calculate PCA projection
pbmc.seurat <- ProjectPCA(pbmc.seurat)
|
| | 0%
|
|===== | 7%
|
|========= | 14%
|
|============== | 21%
|
|=================== | 29%
|
|======================= | 36%
|
|============================ | 43%
|
|================================ | 50%
|
|===================================== | 57%
|
|========================================== | 64%
|
|============================================== | 71%
|
|=================================================== | 79%
|
|======================================================== | 86%
|
|============================================================ | 93%
|
|=================================================================| 100%
[1] "PC1"
[1] "CST3" "S100A9" "FTL" "TYROBP" "FCN1" "LYZ"
[7] "LST1" "AIF1" "S100A8" "FTH1" "TYMP" "LGALS2"
[13] "CFD" "FCER1G" "LGALS1" "CD68" "CTSS" "SERPINA1"
[19] "SAT1" "NPC2" "GRN" "CFP" "IFITM3" "COTL1"
[25] "IFI30" "PSAP" "SPI1" "CD14" "GPX1" "S100A11"
[1] ""
[1] "MALAT1" "RPS27A" "PTPRCAP" "RPSA" "RPS3A" "RPL23A" "IL32"
[8] "RPL3" "RPL9" "RPS27" "CD3D" "LTB" "RPL21" "RPS3"
[15] "RPS6" "RPS15A" "RPL31" "CD3E" "RPL13A" "LDHB" "RPS25"
[22] "RPL30" "RPS12" "RPS18" "RPS23" "RPS29" "RPL27A" "RPL5"
[29] "EEF1A1" "AES"
[1] ""
[1] ""
[1] "PC2"
[1] "NKG7" "GZMB" "PRF1" "CST7" "GZMA" "FGFBP2" "GNLY"
[8] "CTSW" "SPON2" "CCL4" "GZMH" "CCL5" "FCGR3A" "KLRD1"
[15] "XCL2" "CLIC3" "SRGN" "GZMM" "B2M" "CD247" "AKR1C3"
[22] "S100A4" "PRSS23" "TTC38" "S1PR5" "HCST" "IGFBP7" "ITGB2"
[29] "HOPX" "GPR56"
[1] ""
[1] "CD79A" "MS4A1" "RPL18A" "HLA-DQA1" "TCL1A"
[6] "HLA-DQB1" "CD79B" "LINC00926" "LTB" "RPL32"
[11] "VPREB3" "RPL13A" "HLA-DRA" "RPL13" "RPS23"
[16] "RPS27" "RPL11" "RPS18" "RPL8" "RPS5"
[21] "HLA-DQA2" "RPLP2" "RPL12" "RPS2" "CD37"
[26] "FCER2" "CD74" "BANK1" "RPS12" "HLA-DRB1"
[1] ""
[1] ""
[1] "PC3"
[1] "RPL10" "RPS2" "RPL11" "RPL28" "RPL32" "RPL18A" "RPL12"
[8] "RPL19" "RPS6" "TMSB10" "RPS14" "RPL13" "RPS19" "RPS15"
[15] "RPL6" "RPL29" "RPLP1" "RPS3" "RPL15" "RPL26" "RPS4X"
[22] "EEF1A1" "RPS16" "RPS7" "GNB2L1" "RPL14" "RPL13A" "RPL3"
[29] "RPS18" "RPL8"
[1] ""
[1] "PF4" "PPBP" "SDPR" "SPARC"
[5] "GNG11" "HIST1H2AC" "GP9" "NRGN"
[9] "TUBB1" "RGS18" "CLU" "AP001189.4"
[13] "ITGA2B" "CD9" "PTCRA" "TMEM40"
[17] "CA2" "ACRBP" "MMD" "TREML1"
[21] "F13A1" "SEPT5" "PGRMC1" "MYL9"
[25] "TSC22D1" "MPP1" "CMTM5" "PTGS1"
[29] "SNCA" "RP11-367G6.3"
[1] ""
[1] ""
[1] "PC4"
[1] "CD3D" "LDHB" "IL7R" "RPS14" "CD3E" "VIM" "IL32"
[8] "RPL32" "RPS12" "NOSIP" "RPL28" "RPS25" "GIMAP7" "RPL11"
[15] "JUNB" "RPL13" "RPS3" "AQP3" "ZFP36L2" "FYB" "RPL10"
[22] "RGCC" "MAL" "FOS" "LEF1" "RPLP1" "CD2" "RPL35A"
[29] "RPL36" "RPS28"
[1] ""
[1] "CD79A" "HLA-DQA1" "CD79B" "MS4A1" "CD74"
[6] "HLA-DQB1" "HLA-DPB1" "HLA-DPA1" "HLA-DRB1" "TCL1A"
[11] "HLA-DRA" "LINC00926" "HLA-DRB5" "HLA-DQA2" "VPREB3"
[16] "HLA-DMA" "GZMB" "HLA-DMB" "HVCN1" "FCER2"
[21] "BANK1" "FGFBP2" "HLA-DOB" "PDLIM1" "FCRLA"
[26] "TSPAN13" "PRF1" "GNLY" "CD72" "EAF2"
[1] ""
[1] ""
[1] "PC5"
[1] "FCER1A" "LGALS2" "MS4A6A" "S100A8" "CLEC10A" "FOLR3"
[7] "GPX1" "CD14" "GSTP1" "ALDH2" "S100A12" "SERPINF1"
[13] "ID1" "CD1C" "GRN" "RNASE6" "GSN" "IER3"
[19] "CSF3R" "BLVRB" "RPL13" "ASGR1" "S100A9" "RNASE2"
[25] "VCAN" "LYZ" "SAT2" "QPCT" "CEBPD" "FCGR1A"
[1] ""
[1] "FCGR3A" "CDKN1C" "MS4A7" "HES4"
[5] "CKB" "RP11-290F20.3" "RHOC" "CTSL"
[9] "MS4A4A" "LILRA3" "SIGLEC10" "CTD-2006K23.1"
[13] "IFITM3" "HMOX1" "ABI3" "LRRC25"
[17] "IFITM2" "LILRB1" "BATF3" "PTP4A3"
[21] "CEBPB" "PILRA" "CSF1R" "HCK"
[25] "CXCL16" "VMO1" "C1QA" "TPPP3"
[29] "TCF7L2" "TNFSF10"
[1] ""
[1] ""
# Can plot top genes for top components
PrintPCA(pbmc.seurat, pcs.print = 1:2, genes.print = 5, use.full = TRUE)
[1] "PC1"
[1] "CST3" "S100A9" "FTL" "TYROBP" "FCN1"
[1] ""
[1] "MALAT1" "RPS27A" "PTPRCAP" "RPSA" "RPS3A"
[1] ""
[1] ""
[1] "PC2"
[1] "NKG7" "GZMB" "PRF1" "CST7" "GZMA"
[1] ""
[1] "CD79A" "MS4A1" "RPL18A" "HLA-DQA1" "TCL1A"
[1] ""
[1] ""
# Calculate PCA projection
pbmc.seurat <- ProjectPCA(pbmc.seurat)
|
| | 0%
|
|===== | 7%
|
|========= | 14%
|
|============== | 21%
|
|=================== | 29%
|
|======================= | 36%
|
|============================ | 43%
|
|================================ | 50%
|
|===================================== | 57%
|
|========================================== | 64%
|
|============================================== | 71%
|
|=================================================== | 79%
|
|======================================================== | 86%
|
|============================================================ | 93%
|
|=================================================================| 100%
[1] "PC1"
[1] "CST3" "S100A9" "FTL" "TYROBP" "FCN1" "LYZ"
[7] "LST1" "AIF1" "S100A8" "FTH1" "TYMP" "LGALS2"
[13] "CFD" "FCER1G" "LGALS1" "CD68" "CTSS" "SERPINA1"
[19] "SAT1" "NPC2" "GRN" "CFP" "IFITM3" "COTL1"
[25] "IFI30" "PSAP" "SPI1" "CD14" "GPX1" "S100A11"
[1] ""
[1] "MALAT1" "RPS27A" "PTPRCAP" "RPSA" "RPS3A" "RPL23A" "IL32"
[8] "RPL3" "RPL9" "RPS27" "CD3D" "LTB" "RPL21" "RPS3"
[15] "RPS6" "RPS15A" "RPL31" "CD3E" "RPL13A" "LDHB" "RPS25"
[22] "RPL30" "RPS12" "RPS18" "RPS23" "RPS29" "RPL27A" "RPL5"
[29] "EEF1A1" "AES"
[1] ""
[1] ""
[1] "PC2"
[1] "NKG7" "GZMB" "PRF1" "CST7" "GZMA" "FGFBP2" "GNLY"
[8] "CTSW" "SPON2" "CCL4" "GZMH" "CCL5" "FCGR3A" "KLRD1"
[15] "XCL2" "CLIC3" "SRGN" "GZMM" "B2M" "CD247" "AKR1C3"
[22] "S100A4" "PRSS23" "TTC38" "S1PR5" "HCST" "IGFBP7" "ITGB2"
[29] "HOPX" "GPR56"
[1] ""
[1] "CD79A" "MS4A1" "RPL18A" "HLA-DQA1" "TCL1A"
[6] "HLA-DQB1" "CD79B" "LINC00926" "LTB" "RPL32"
[11] "VPREB3" "RPL13A" "HLA-DRA" "RPL13" "RPS23"
[16] "RPS27" "RPL11" "RPS18" "RPL8" "RPS5"
[21] "HLA-DQA2" "RPLP2" "RPL12" "RPS2" "CD37"
[26] "FCER2" "CD74" "BANK1" "RPS12" "HLA-DRB1"
[1] ""
[1] ""
[1] "PC3"
[1] "RPL10" "RPS2" "RPL11" "RPL28" "RPL32" "RPL18A" "RPL12"
[8] "RPL19" "RPS6" "TMSB10" "RPS14" "RPL13" "RPS19" "RPS15"
[15] "RPL6" "RPL29" "RPLP1" "RPS3" "RPL15" "RPL26" "RPS4X"
[22] "EEF1A1" "RPS16" "RPS7" "GNB2L1" "RPL14" "RPL13A" "RPL3"
[29] "RPS18" "RPL8"
[1] ""
[1] "PF4" "PPBP" "SDPR" "SPARC"
[5] "GNG11" "HIST1H2AC" "GP9" "NRGN"
[9] "TUBB1" "RGS18" "CLU" "AP001189.4"
[13] "ITGA2B" "CD9" "PTCRA" "TMEM40"
[17] "CA2" "ACRBP" "MMD" "TREML1"
[21] "F13A1" "SEPT5" "PGRMC1" "MYL9"
[25] "TSC22D1" "MPP1" "CMTM5" "PTGS1"
[29] "SNCA" "RP11-367G6.3"
[1] ""
[1] ""
[1] "PC4"
[1] "CD3D" "LDHB" "IL7R" "RPS14" "CD3E" "VIM" "IL32"
[8] "RPL32" "RPS12" "NOSIP" "RPL28" "RPS25" "GIMAP7" "RPL11"
[15] "JUNB" "RPL13" "RPS3" "AQP3" "ZFP36L2" "FYB" "RPL10"
[22] "RGCC" "MAL" "FOS" "LEF1" "RPLP1" "CD2" "RPL35A"
[29] "RPL36" "RPS28"
[1] ""
[1] "CD79A" "HLA-DQA1" "CD79B" "MS4A1" "CD74"
[6] "HLA-DQB1" "HLA-DPB1" "HLA-DPA1" "HLA-DRB1" "TCL1A"
[11] "HLA-DRA" "LINC00926" "HLA-DRB5" "HLA-DQA2" "VPREB3"
[16] "HLA-DMA" "GZMB" "HLA-DMB" "HVCN1" "FCER2"
[21] "BANK1" "FGFBP2" "HLA-DOB" "PDLIM1" "FCRLA"
[26] "TSPAN13" "PRF1" "GNLY" "CD72" "EAF2"
[1] ""
[1] ""
[1] "PC5"
[1] "FCER1A" "LGALS2" "MS4A6A" "S100A8" "CLEC10A" "FOLR3"
[7] "GPX1" "CD14" "GSTP1" "ALDH2" "S100A12" "SERPINF1"
[13] "ID1" "CD1C" "GRN" "RNASE6" "GSN" "IER3"
[19] "CSF3R" "BLVRB" "RPL13" "ASGR1" "S100A9" "RNASE2"
[25] "VCAN" "LYZ" "SAT2" "QPCT" "CEBPD" "FCGR1A"
[1] ""
[1] "FCGR3A" "CDKN1C" "MS4A7" "HES4"
[5] "CKB" "RP11-290F20.3" "RHOC" "CTSL"
[9] "MS4A4A" "LILRA3" "SIGLEC10" "CTD-2006K23.1"
[13] "IFITM3" "HMOX1" "ABI3" "LRRC25"
[17] "IFITM2" "LILRB1" "BATF3" "PTP4A3"
[21] "CEBPB" "PILRA" "CSF1R" "HCK"
[25] "CXCL16" "VMO1" "C1QA" "TPPP3"
[29] "TCF7L2" "TNFSF10"
[1] ""
[1] ""
# Can plot top genes for top components
PrintPCA(pbmc.seurat, pcs.print = 1:2, genes.print = 5, use.full = TRUE)
[1] "PC1"
[1] "CST3" "S100A9" "FTL" "TYROBP" "FCN1"
[1] ""
[1] "MALAT1" "RPS27A" "PTPRCAP" "RPSA" "RPS3A"
[1] ""
[1] ""
[1] "PC2"
[1] "NKG7" "GZMB" "PRF1" "CST7" "GZMA"
[1] ""
[1] "CD79A" "MS4A1" "RPL18A" "HLA-DQA1" "TCL1A"
[1] ""
[1] ""
VizPCA(pbmc.seurat, pcs.use=1:2)
How can we apply this?
PCAPlot(pbmc.seurat, 1, 2)
PCHeatmap(pbmc.seurat, pc.use=1, cells.use=100, do.balanced=TRUE)
# Time Intensive
# Jackstraw
# pbmc.seurat <- JackStraw(pbmc.seurat, num.replicate = 100, do.print = FALSE)
How do we choose how many components to use?
# Scree (elbow) plot
PCElbowPlot(pbmc.seurat)
# 1 minute
pbmc.seurat <- FindClusters(pbmc.seurat, pc.use = 1:10, resolution = 0.6, print.output = 0, save.SNN = TRUE)
# Calculate t-SNE Ordination
pbmc.seurat <- RunTSNE(pbmc.seurat, dims.use = 1:10, do.fast = TRUE)
# Plot
TSNEPlot(pbmc.seurat)
This is not PCA
PCA
t-SNE
Now that we have subclusters of cell populations plotting genes through subclusters is identical to before.
VlnPlot(pbmc.seurat, c("MS4A1","CD79A"))
You can also gene expression through out the cell ordination.
FeaturePlot(pbmc.seurat, c("MS4A1","CD3E", "GNLY", "FCER1A"), cols.use = c("grey","blue"))
It is important to know cells are expressing expected genes.
FeaturePlot(pbmc.seurat, c("nGene"), cols.use = c("grey","blue"))
Check for your batch affect.
# Making Fake Data
fake.sites <- as.integer(pbmc.seurat@ident %in% c(5,2,8,7))
names(fake.sites) <- colnames(pbmc.seurat@data)
# Add metadata
pbmc.seurat <- AddMetaData(pbmc.seurat, fake.sites, "site")
# Plot feature
FeaturePlot(pbmc.seurat, c("site"), cols.use = c("green","orange"))
cell.labels <- pbmc.seurat@ident
corner(cell.labels)
AAACATACAACCAC AAACATTGAGCTAC AAACATTGATCAGC AAACCGTGCTTCCG AAACCGTGTATGCG
0 2 0 1 5
Levels: 0 1 2 3 4 5 6 7
cluster1.markers <- FindMarkers(pbmc.seurat, ident.1 = 1, min.pct = 0.25)
head(cluster1.markers, 5)
pbmc.markers <- FindAllMarkers(pbmc.seurat, only.pos = TRUE, min.pct = 0.25, thresh.use = 0.25)
pbmc.markers %>% group_by(cluster) %>% top_n(2, avg_diff)
Source: local data frame [16 x 6]
Groups: cluster [8]
p_val avg_diff pct.1 pct.2 cluster gene
<dbl> <dbl> <dbl> <dbl> <fctr> <chr>
1 0 1.148598 0.925 0.482 0 LDHB
2 0 1.064824 0.662 0.202 0 IL7R
3 0 3.822197 0.996 0.217 1 S100A9
4 0 3.784692 0.975 0.123 1 S100A8
5 0 2.977214 0.936 0.042 2 CD79A
6 0 2.485397 0.623 0.022 2 TCL1A
7 0 2.171555 0.974 0.230 3 CCL5
8 0 2.113108 0.588 0.050 3 GZMK
9 0 2.268441 0.962 0.137 4 FCGR3A
10 0 2.150098 1.000 0.316 4 LST1
11 0 3.763449 0.961 0.131 5 GNLY
12 0 3.333932 0.955 0.068 5 GZMB
13 0 2.735429 0.861 0.009 6 FCER1A
14 0 2.013750 0.944 0.208 6 HLA-DQA1
15 0 5.872560 1.000 0.023 7 PPBP
16 0 4.949401 1.000 0.010 7 PF4
bimod: Tests differences in mean and proportions.
roc: Uses AUC like definition of separation.
t: Student's T-test.
tobit: Tobit regression on a smoothed data.
pbmc.markers %>% group_by(cluster) %>% top_n(10, avg_diff) -> top10
DoHeatmap(pbmc.seurat, genes.use = top10$gene, order.by.ident = TRUE, slim.col.label = TRUE, remove.key = TRUE)
pbmc.markers %>% group_by(cluster) %>% top_n(10, avg_diff) -> top10
DoHeatmap(pbmc.seurat, genes.use = top10$gene, order.by.ident = TRUE, slim.col.label = TRUE, remove.key = TRUE)
For each group (ES or MEF).
Differential Expression.
data(es.mef.small)
dim(es.mef.small)
[1] 14897 40
cd <- clean.counts(es.mef.small, min.lib.size=1000, min.reads = 1, min.detected = 1)
dim(cd)
[1] 12142 40
## Setting up cells groups
data.groups <- rep(NA, ncol(es.mef.small))
data.groups[ grep("MEF", names(es.mef.small)) ] <- "MEF"
data.groups[ grep("ESC", names(es.mef.small)) ] <- "ESC"
data.groups <- factor(data.groups, levels = c("ESC","MEF"))
names(data.groups) <- colnames(es.mef.small)
table(data.groups)
data.groups
ESC MEF
20 20
## Calculate error models
## Time Intensive step
# o.ifm <- scde.error.models(counts=cd, groups= data.groups, n.cores=4,
# threshold.segmentation=TRUE, save.crossfit.plots=FALSE,
# save.model.plots=FALSE, verbose=1)
## Precomputed
data(o.ifm)
## Calculate error models
## Time Intensive step
# o.ifm <- scde.error.models(counts=cd, groups= data.groups, n.cores=4,
# threshold.segmentation=TRUE, save.crossfit.plots=FALSE,
# save.model.plots=FALSE, verbose=1)
# Check number of cores
detectCores()
[1] 8
# Error model coefficients (cells = rows)
# corr.a = slope of the correlated component fit
# Negative corr.a could be bad cells
# corr.b intercept of the correlated component fit
# corr.theta is NB over-dispersion
# fail.r background poisson rate
head(o.ifm)
conc.b conc.a fail.r corr.b corr.a corr.theta
ESC_10 -1.449443 0.5639140 -2.302585 0.7148157 0.6496142 0.7732069
ESC_11 -3.244421 0.7327046 -2.302585 1.5918205 0.5351960 0.7070433
ESC_12 -4.472559 0.8073935 -2.302585 1.5203470 0.4909147 0.7372590
ESC_13 -5.208909 0.8804523 -2.302585 1.2539230 0.5242493 0.8215473
ESC_14 -4.124369 0.7794612 -2.302585 1.1127353 0.5620266 0.7456712
ESC_15 -5.410838 0.9324758 -2.302585 1.1571732 0.5482784 0.7712750
dim(o.ifm)
[1] 40 6
valid.cells <- o.ifm$corr.a > 0
table(valid.cells)
valid.cells
TRUE
40
o.ifm <- o.ifm[valid.cells, ]
dim(o.ifm)
[1] 40 6
## Calculate the Prior (starting value)
o.prior <- scde.expression.prior(models=o.ifm, counts=cd, length.out=400, show.plot=FALSE)
## Setting up cells groups
data.groups <- rep(NA, nrow(o.ifm))
data.groups[ grep("MEF", rownames(o.ifm)) ] <- "MEF"
data.groups[ grep("ESC", rownames(o.ifm)) ] <- "ESC"
data.groups <- factor(data.groups, levels = c("ESC","MEF"))
names(data.groups) <- row.names(o.ifm)
## Perform T-test like analysis
# 2 minutes on standard computer, 1 core
#ediff <- scde.expression.difference(o.ifm, cd, o.prior, groups=data.groups, n.randomizations=100, n.cores=2, verbose=1)
load("data/ediff.Robj")
head(ediff[order(ediff$Z, decreasing = TRUE), ])
lb mle ub ce Z cZ
Dppa5a 8.075220 9.984631 11.575807 8.075220 7.160813 5.989598
Pou5f1 5.370220 7.200073 9.189043 5.370220 7.160328 5.989598
Gm13242 5.688455 7.677425 9.785734 5.688455 7.159979 5.989598
Tdh 5.807793 8.075220 10.302866 5.807793 7.159589 5.989598
Ift46 5.449779 7.359190 9.228822 5.449779 7.150242 5.989598
4930509G22Rik 5.409999 7.478528 9.785734 5.409999 7.115605 5.978296
write.table(ediff[order(abs(ediff$Z), decreasing = TRUE), ],
file = "data/scde_results.txt", row.names = TRUE, col.names = TRUE, sep = "\t", quote = FALSE)
scde.test.gene.expression.difference("Tdh", models = o.ifm, counts = cd, prior = o.prior)
lb mle ub ce Z cZ
Tdh 5.728235 8.03544 10.30287 5.728235 7.151425 7.151425
# scde.browse.diffexp(ediff, o.ifm, cd, o.prior, groups = groups, name = "diffexp1", port = 1299)
batch <- as.factor(ifelse(rbinom(nrow(o.ifm), 1, 0.5) == 1, "batch1", "batch2"))
table(groups, batch)
scde.test.gene.expression.difference("Tdh", models = o.ifm, counts = cd, prior = o.prior, batch = batch)
ediff.batch <- scde.expression.difference(o.ifm, cd, o.prior, groups = groups, batch = batch, n.randomizations = 100, n.cores = 1, return.posteriors = TRUE, verbose = 1)
Fan et al.
data(pollen)
# Original genes and cells (count matrix)
dim(pollen)
[1] 23710 64
# Filter poor cells
pollen.clean <- clean.counts(pollen)
# Cleaned matrix dimensions
dim(pollen.clean)
[1] 11310 64
name.keys <- gsub("^Hi_(.*)_.*", "\\1", colnames(pollen.clean))
name.keys
[1] "NPC" "NPC" "NPC" "NPC" "NPC" "NPC" "NPC"
[8] "NPC" "NPC" "NPC" "NPC" "NPC" "NPC" "NPC"
[15] "NPC" "GW16" "GW16" "GW21" "GW21+3" "GW21+3" "GW16"
[22] "GW21+3" "GW21+3" "GW16" "GW16" "GW16" "GW16" "GW16"
[29] "GW16" "GW16" "GW16" "GW16" "GW16" "GW21" "GW21"
[36] "GW16" "GW16" "GW21" "GW16" "GW16" "GW16" "GW16"
[43] "GW16" "GW21" "GW21" "GW21" "GW21+3" "GW16" "GW16"
[50] "GW16" "GW16" "GW16" "GW21" "GW21+3" "GW21+3" "GW21+3"
[57] "GW21+3" "GW21+3" "GW21+3" "GW21+3" "GW21+3" "GW21+3" "GW21+3"
[64] "GW21+3"
l2cols <- c("coral4", "olivedrab3", "skyblue2", "slateblue3")[as.integer(factor(name.keys,
levels = c("NPC", "GW16", "GW21", "GW21+3")))]
l2cols
[1] "coral4" "coral4" "coral4" "coral4" "coral4"
[6] "coral4" "coral4" "coral4" "coral4" "coral4"
[11] "coral4" "coral4" "coral4" "coral4" "coral4"
[16] "olivedrab3" "olivedrab3" "skyblue2" "slateblue3" "slateblue3"
[21] "olivedrab3" "slateblue3" "slateblue3" "olivedrab3" "olivedrab3"
[26] "olivedrab3" "olivedrab3" "olivedrab3" "olivedrab3" "olivedrab3"
[31] "olivedrab3" "olivedrab3" "olivedrab3" "skyblue2" "skyblue2"
[36] "olivedrab3" "olivedrab3" "skyblue2" "olivedrab3" "olivedrab3"
[41] "olivedrab3" "olivedrab3" "olivedrab3" "skyblue2" "skyblue2"
[46] "skyblue2" "slateblue3" "olivedrab3" "olivedrab3" "olivedrab3"
[51] "olivedrab3" "olivedrab3" "skyblue2" "slateblue3" "slateblue3"
[56] "slateblue3" "slateblue3" "slateblue3" "slateblue3" "slateblue3"
[61] "slateblue3" "slateblue3" "slateblue3" "slateblue3"
# knn <- knn.error.models(pollen.clean, k=ncol(pollen.clean)/4, n.cores=2,
# min.count.threshold=2, min.nonfailed=5, max.model.plots=10) Precomputed
# data
data(knn)
# varinfo <- pagoda.varnorm(knn, counts=pollen.clean,
# trim=3/ncol(pollen.clean), max.adj.var=5, n.cores=1, plot=TRUE)
load("data/varinfo.Robj")
# list top overdispersed genes
sort(varinfo$arv, decreasing = TRUE)[1:10]
# Control for complexity
varinfo <- pagoda.subtract.aspect(varinfo, colSums(pollen.clean[, rownames(knn)] >
0))
# varinfo <- pagoda.varnorm(knn, counts=pollen.clean,
# trim=3/ncol(pollen.clean), max.adj.var=5, n.cores=2, plot=TRUE)
load("data/varinfo.Robj")
# list top overdispersed genes
sort(varinfo$arv, decreasing = TRUE)[1:10]
DCX EGR1 FOS IGFBPL1 MALAT1 MEF2C STMN2 TOP2A
5.000000 5.000000 5.000000 5.000000 5.000000 5.000000 5.000000 5.000000
BCL11A SOX4
4.739497 4.489101
# Control for complexity
varinfo <- pagoda.subtract.aspect(varinfo, colSums(pollen.clean[, rownames(knn)] >
0))
library(org.Hs.eg.db)
# translate gene names to ids
ids <- unlist(lapply(mget(rownames(pollen.clean), org.Hs.egALIAS2EG, ifnotfound = NA),
function(x) x[1]))
rids <- names(ids)
names(rids) <- ids
# convert GO lists from ids to gene names
gos.interest <- unique(c(ls(org.Hs.egGO2ALLEGS)[1:100], "GO:0022008", "GO:0048699",
"GO:0000280", "GO:0007067"))
go.env <- lapply(mget(gos.interest, org.Hs.egGO2ALLEGS), function(x) as.character(na.omit(rids[x])))
go.env <- clean.gos(go.env) # remove GOs with too few or too many genes
go.env <- list2env(go.env) # convert to an environment
# pwpca <- pagoda.pathway.wPCA(varinfo, go.env, n.components = 1, n.cores =
# 1)
load("data/pwpca.Robj")
df <- pagoda.top.aspects(pwpca, return.table = TRUE, plot = TRUE, z.score = 1.96)
head(df)
# clpca <- pagoda.gene.clusters(varinfo, trim = 7.1/ncol(varinfo$mat),
# n.clusters = 50, n.cores = 1, plot = TRUE)
load("data/clpca.Robj")
df <- pagoda.top.aspects(pwpca, clpca, return.table = TRUE, plot = TRUE, z.score = 1.96)
head(df)
name npc n score z adj.z sh.z adj.sh.z
56 geneCluster.6 1 391 3.289412 13.814757 13.530139 NA NA
49 GO:0000280 1 428 1.587359 11.592060 11.252131 NA NA
50 GO:0007067 1 362 1.585331 10.873998 10.576419 NA NA
79 geneCluster.29 1 171 1.583733 5.162516 4.523195 NA NA
14 GO:0000070 1 116 1.446979 5.625549 5.247594 NA NA
89 geneCluster.39 1 178 1.392980 4.137589 3.438496 NA NA
# Get full info on the top aspects tam <- pagoda.top.aspects(pwpca, clpca,
# n.cells = NULL, z.score = qnorm(0.01/2, lower.tail = FALSE))
load("data/tam.Robj")
# Determine overall cell clustering
hc <- pagoda.cluster.cells(tam, varinfo)
# tamr <- pagoda.reduce.loading.redundancy(tam, pwpca, clpca)
load("data/tamr.Robj")
# tamr2 <- pagoda.reduce.redundancy(tamr, distance.threshold = 0.9, plot =
# TRUE, cell.clustering = hc, labRow = NA, labCol = NA, box = TRUE, margins
# = c(0.5, 0.5), trim = 0)
load("data/tamr2.Robj")
col.cols <- rbind(groups = cutree(hc, 3))
pagoda.view.aspects(tamr2, cell.clustering = hc, box = TRUE, labCol = NA, margins = c(0.5,
20), col.cols = rbind(l2cols))
## compile a browsable app, showing top three clusters with the top color bar
## app <- make.pagoda.app(tamr2, tam, varinfo, go.env, pwpca, clpca, col.cols
## = col.cols, cell.clustering = hc, title = 'NPCs') show app in the browser
## (port 1468) show.app(app, 'pollen', browse = TRUE, port = 1468)
# pdf( 'data/my_file.pdf', useDingbats = FALSE ) # Start pdf plot( 1:10,
# log(1:10 ) ) # plot in to the pdf file plot( seq(0,.9,.1), sin(0:9) ) #
# another plot for the pdf file dev.off() # Close pdf file ( very important
# )